Multimarker risk stratification

ABSTRACT

Measurement of circulating ST2 and natriuretic peptide (e.g., NT-proBNP) concentrations is useful for the prognostic evaluation of subjects, in particular for the prediction of adverse clinical outcomes, e.g., mortality, transplantation, and heart failure.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.15/385,095, filed on Dec. 20, 2016, which is a continuation of U.S.patent application Ser. No. 13/972,596, filed on Aug. 21, 2013, nowabandoned, and claims priority to U.S. Provisional Patent ApplicationSer. No. 61/691,706, filed on Aug. 21, 2012. The entire contents of theforegoing are incorporated by reference herein.

TECHNICAL FIELD

The invention relates to methods for predicting risk of mortality, insubjects with cardiovascular disease, e.g., heart failure, based onmultiple markers including a combination of biomarkers (e.g., ST2) andother clinical parameters (e.g., age).

BACKGROUND

Clinical evaluation for determination of risk of mortality due to heartfailure may not always be straightforward. The decision whether to treata subject aggressively or conservatively, or to admit the subject as aninpatient or to send them home, may sometimes be made solely on aphysician's clinical assessment or “gut feeling” as to the individual'sactual condition. A formula for determining a subject's likelihood ofmortality would significantly enhance the physician's ability to makeinformed treatment decisions, improve patient care and reduce overallhealthcare costs. A multi-marker approach for risk stratification hasbeen generally proposed for patients with acute coronary syndromes, see,e.g., Sabatine et al., Circulation 105(15): 1760-3 (2002)), and methodsfor predicting risk of a major adverse cardiac event are describe inU.S. Pat. No. 8,090,562.

SUMMARY

The present invention is based, at least in part, on the discovery thatmultiple markers, including serum levels of the biomarker ST2 (alsoknown as Interleukin 1 Receptor Like 1 (IL1RL-1)), in combination withclinical parameters such as age and levels of at least one otherbiomarker, e.g., troponin or a natriuretic peptide (NP) such as theinactive N-terminal fragment of brain-type natriuretic peptide(NT-pro-BNP), can be used predict the likelihood of mortality due to CVDwithin a specific time period, e.g., 30 days, 3 or 6 months, or a yearor more (e.g., 2, 5 or 10 years).

Provided herein are methods of evaluating the risk of mortality for asubject (e.g., a subject having or diagnosed with heart failure) withina specific time period (e.g., within 3 months, 6 months, or a year ormore (e.g., 2, 5, or 10 years) that include determining a multimarkermortality risk score for a subject based upon the age of the subject;the level of ST2 in the subject, in combination with one or more of anatural logarithm of a level of a brain natriuretic peptide (BNP) in thesubject; a level of troponin in the subject; a New York HeartAssociation (NYHA) score; a history of cardiovascular disease (CAD); anatural logarithm of a systolic blood pressure; a measure of renalfunction or a natural logarithm of a level of hemoglobin (Hgb), and age;and comparing the multimarker mortality risk score to a referencemultimarker mortality risk score; wherein the presence of a multimarkermortality risk score that is at or above the reference multimarkermortality risk score indicates that the subject has an increased risk ofmortality within the specific time period, and the presence of amultimarker mortality risk score that is below the reference multimarkermortality risk score indicates that the subject has a decreased risk ofmortality within the specific time period (e.g., within one year).

In some embodiments, the risk score is determined using one of thefollowing algorithms:AGE+ST2+ln_SBP+CAD+ln_NTpro-BNP  (1)AGE+ST2+ln_NTpro-BNP  (2)AGE+ST2+Troponin+NYHA  (3)AGE+ST2+[Troponin OR NYHA]  (4)AGE+ST2+[Troponin AND/OR NYHA]+ln_Hgb  (5)AGE+ST2+[Troponin AND/OR NYHA]+ln_Hgb  (6)AGE+ST2+[Troponin AND/OR NYHA]+ln_Hgb+ln_SBP  (7)AGE+ST2+[Troponin AND/OR NYHA]+ln_Hgb+ln_SBP+ln_NTpro-BNP.  (8)

In some embodiments, the level of ST2 is determined and compared to athreshold and the presence of a level at or above the threshold isscored as “1” and the presence of a level below the threshold is scoredas “0”. In some embodiments, the threshold level of ST2 is 35 or 50ng/mL. In some embodiments, algorithm (1) or (2) is used and thethreshold level of ST2 is 35 ng/mL. In some embodiments, algorithm (3)or (4) is used and the threshold level of ST2 is 50 ng/mL. In someembodiments, the subject has been diagnosed with a cardiovasculardisease (e.g., heart failure). In some embodiments, the referencemultimarker mortality risk score represents a score corresponding to alow risk of death within a specific time period (e.g., within 3 months,6 months, 1, 2, 5 or 10 years). In some embodiments, the sample containsserum, blood, plasma, urine, or body tissue.

In some embodiments, the subject has a BMI of 25-29, a BMI of ≥30, orrenal insufficiency. Some embodiments further include discharging thesubject or treating the subject on an inpatient basis based on thepresence of an increased risk of mortality determined using any of themethods described herein. For example, a subject identified as having anincreased risk of mortality within the specific time period (e.g.,within 3 months, 6 months, 1, 2, 5 or 10 years) is treated on aninpatient basis (e.g., newly admitted to a hospital or continuedhospitalization) or a subject identified as having a decreased risk ofmortality within the specific time period (e.g., within 3 months, 6months, 1, 2, 5 or 10 years) is discharged from a hospital or continuedto be treated on an outpatient basis. Some embodiments further includeselecting and/or performing increased cardiac monitoring (e.g., any ofthe examples of increased cardiac monitoring described herein or knownin the art) on a subject identified as having an increased risk ofmortality within the specific time period (e.g., using any of themethods described herein), or selecting and/or performing low frequencymonitoring (e.g., cardiac monitoring) on a subject (e.g., greater than 6months between examinations, greater than 9 months between examinations,or one year or greater between examinations) identified as having areduced risk of mortality within the specific time period (e.g., usingany of the methods described herein). As described herein, increasedcardiac monitoring can be, e.g., the monitoring of cardiac function in asubject (e.g., electrocardiogram (e.g., ambulatory electrocardiography),chest X-ray, echocardiography, stress testing, computer tomography,magnetic resonance imaging, positron emission tomography, and cardiaccatheterization) or the monitoring of levels of soluble ST2 in thesubject over time. Increased cardiac monitoring can also includeincreased frequency of clinical visits (e.g., about once every month,once every two months, once every three months, once every four months,once every five months, or once every six months). Also provided aremethods of selecting a treatment for a subject receiving a treatment fora cardiovascular disorder that include determining the subject's risk ofmortality over a specific time period (e.g., within any of the timeperiods described herein, e.g., within 3 months, 6 months, 1, 2, 5 or 10years) using any of the methods described herein, and selectingcontinuation of the treatment for a subject determined to have a reducedrisk of mortality over the specific time period (e.g., using any of themethods described herein) or selecting a new (alternate) cardiovasculartreatment for a subject determined to have an increased risk ofmortality over the specific time period (e.g., using any of the methodsdescribed herein). As described herein, a new treatment can meanadministration of a new combination therapeutic agents, administrationof a new therapeutic agent, a different dosage of the previouslyadministered therapeutic agent, a different frequency of administrationof the previously administered therapeutic agent, or a different routeof administration of the previously administered therapeutic agent. Someembodiments further include administering the selected treatment to asubject.

Also provided are methods of selecting a subject for a clinical studythat include determining a subject's risk of mortality within a specifictime period (e.g., any of the specific time periods described herein,e.g., within 3 months, 6 months, 1, 2, 5 or 10 years) (e.g., using anyof the methods described herein) and selecting a subject determined tohave an increased risk of mortality within the specific time period forparticipation in a clinical study.

Also provided herein are methods of determining whether a subject's riskof mortality (e.g., caused by a cardiovascular disorder) is increasingor decreasing over time. These methods include determining a firstmultimarker mortality risk score in a subject at a first time point(e.g., using any of the methods described herein), determining a secondmultimarker risk score in a subject at a second time point (e.g., usingany of the methods described herein), comparing the second multimarkerrisk score to the first multimarker risk score, and identifying asubject having an elevated second multimarker risk score as compared tothe first multimarker risk score as having an increasing risk ofmortality over time or identifying a subject having a decreased secondmultimarker risk score as compared to the first multimarker risk scoreas having a decreasing risk of mortality over time.

Also provided are methods of determining the efficacy of a treatment fora cardiovascular disorder (e.g., heart failure) in a subject thatinclude, determining a first multimarker risk score in a subject at afirst time point (e.g., using any of the methods described herein),determining a second multimarker risk score in a subject at a secondtime point (e.g., using any of the methods described herein), where twoor more doses of a treatment for a cardiovascular disorder (e.g., heartfailure) are administered to the subject between the first and thesecond time points, comparing the second multimarker risk score to thefirst multimarker risk score, and identifying the treatment as effectivein a subject having a decreased second multimarker risk score ascompared to the first multimarker risk score, or identifying thetreatment as not being effective in a subject having an elevated secondmultimarker risk score as compared to the first multimarker risk score.Some embodiments further include selecting the treatment identified asbeing effective in the subject, and/or continuing to administer theselected treatment to the subject.

Also provided are methods of selecting a treatment for a subject thatinclude determining a first multimarker risk score for a subject at afirst time point (e.g., using any of the methods described herein),determining a second multimarker risk score for a subject at a secondtime point (e.g., using any of the methods described herein), comparingthe second multimarker risk score with the first multimarker risk score,and selecting inpatient treatment (e.g., initial hospital admission orcontinued inpatient treatment) for a subject having an elevated secondmultimarker risk score as compared to first multimarker risk score orselecting outpatient treatment (e.g., hospital discharge or continuedoutpatient treatment) for a subject having a decreased secondmultimarker risk score as compared to the first multimarker risk score.Some methods further include admitting the subject to the hospital,continuing inpatient treatment, discharging the subject, or continuingoutpatient treatment based on the comparison of the second and firstmultimarker risk scores (e.g., as selected above).

Also provided are methods of selecting a treatment for a subject thatinclude determining a first multimarker risk score for a subject at afirst time point (e.g., using any of the methods described herein),determining a second multimarker risk score for a subject at a secondtime point (e.g., using any of the methods described herein), comparingthe second multimarker risk score to the first multimarker risk score,and selecting increased cardiac monitoring for a subject having anelevated second multimarker risk score as compared to the firstmultimarker risk score or selecting low frequency monitoring (e.g.,cardiac monitoring) (e.g., greater than 6 months between examinations,greater than 9 months between examinations, or one year or greaterbetween examinations) for a subject having a decreased secondmultimarker risk score as compared to the first multimarker risk score.As described herein, increased cardiac monitoring can be, e.g., themonitoring of cardiac function in a subject (e.g., electrocardiogram(e.g., ambulatory electrocardiography), chest X-ray, echocardiography,stress testing, computer tomography, magnetic resonance imaging,positron emission tomography, and cardiac catheterization) or themonitoring of the levels of soluble ST2 in the subject over time.Increased cardiac monitoring can also include increased frequency ofclinical visits (e.g., about once every month, once every two months,once every three months, once every four months, once every five months,or once every six months). Some methods further include administeringthe selected treatment to the subject.

Also provided are methods of selecting a treatment for a subject thatinclude determining a first multimarker risk score in a subject at afirst time point (e.g., using any of the methods described herein),determining a second multimarker risk score in the subject at a secondtime point (e.g., using any of the methods described herein), where asubject has been administered at least two doses of treatment (e.g., atreatment of a cardiovascular disease) between the first time point andthe second time point, comparing the first multimarker risk score to thesecond multimarker risk score, and selecting a new treatment for asubject having an elevated second multimarker risk score as compared tothe first multimarker risk score or selecting the same treatment for asubject having a decreased second multimarker risk score compared to thefirst multimarker risk score. Some embodiments further includeadministering the selected treatment to the subject. As describedherein, a new treatment can mean administration of a new combinationtherapeutic agents, administration of a new therapeutic agent, adifferent dosage of the previously administered therapeutic agent, adifferent frequency of administration of the previously administeredtherapeutic agent, or a different route of administration of thepreviously administered therapeutic agent.

Also provided are methods of selecting a subject for participation in aclinical study of a treatment for cardiovascular disease that includedetermining a first multimarker risk score in a subject at a first timepoint (e.g., using any of the methods described herein), determining asecond multimarker risk score in the subject at a second time point(e.g., using any of the methods described herein), and selecting asubject having an elevated second multimarker risk score as compared tofirst multimarker risk score for participation in a clinical study of acardiovascular disease.

As used herein, a “sample” includes any bodily fluid or tissue, e.g.,one or more of blood, serum, plasma, urine, and body tissue. In certainembodiments, a sample is a serum, plasma, or blood sample.

An antibody that “binds specifically to” an antigen, bindspreferentially to the antigen in a sample containing other proteins.

The methods and kits described herein have a number of advantages. Forexample, the methods can be used to determine whether a patient shouldbe admitted or held as an inpatient for further assessment, regardlessof whether a definitive diagnosis has been made. For example, themethods can be used for risk stratification of a given subject, e.g., tomake decisions regarding the level of aggressiveness of treatment thatis appropriate for the subject, based on their multimarker risk score asdetermined by a method described herein. Better treatment decisions canlead to reduced morbidity and mortality, and better allocation of scarcehealth care resources. The methods described herein can be used to makegeneral assessments as to whether a patient should be further tested todetermine a specific diagnosis. The methods described herein can also beused for patient population risk stratification, e.g., to provideinformation about clinical performance or expected response to atherapeutic intervention. The methods described herein can be usedregardless of the underlying cause or ultimate diagnosis, and thereforeare not limited to specific indications.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Methods and materials aredescribed herein for use in the present invention; other, suitablemethods and materials known in the art can also be used. The materials,methods, and examples are illustrative only and not intended to belimiting.

All publications, patent applications, patents, sequences, databaseentries, and other references mentioned herein are incorporated byreference in their entirety. In addition, the present applicationincorporates by reference the entire contents of U.S. patent applicationSer. No. 11/789,169, and international patent application nos.PCT/US2007/067626, PCT/US2007/067914, and PCT/US2007/068024.

In case of conflict, the present specification, including definitions,will control.

Other features and advantages of the invention will be apparent from thefollowing detailed description and Figures, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 shows the summary statistics for individual variables for 1year-mortality.

FIG. 2 shows the summary statistics for individual variables for 5-yearmortality.

FIGS. 3-24 show linearity checks and cut-point evaluations performed foreach variable.

FIGS. 25 and 26 provide a summary of the results for each variable.

FIGS. 27-34 show the results of several heuristic approaches used toidentify the best models for predicting risk of death, includingbackward, forward, and stepwise selection. Selection in each instancewas made based on AIC (Akaike's Information Criteria) or BIC (BayesianInformation Criteria).

FIG. 35 shows the co-linearity analysis of several variables with riskof death.

FIG. 36 is a summary of the univariate performance of each variable.

FIGS. 37-49 show the results of linearity checks performed for eachvariable.

FIG. 50 provides a summary of the results for each variable.

FIG. 51 shows AIC-based marker selection.

FIG. 52 shows BIC-based marker selection.

FIGS. 53 and 54 show a comparison of two models([Age+Ln_SBP+CAD+ST2>=35+LN_NTBNP] and [Age+ST2>=35+LN_NTBNP].

FIG. 55 shows bootstrap AUC estimates for the 5-parameter and the3-parameter model.

FIG. 56 is a graph showing the model calibration for the 5-paramenterand the 3-parameter model.

FIG. 57 is list of exemplary model parameters.

DETAILED DESCRIPTION

Clinical evaluation of patients, particularly patients with non-specificsymptoms such as dyspnea or chest pain, is often challenging. Theresults described herein provide evidence that multimarker risk scoresbased on multiple markers including the subject's age and levels of ST2,plus additional clinical parameters including one or more of: systolicblood pressure, the presence of coronary artery disease, New York HeartAssociation (NYHA) score, measures of renal function, levels of troponinand/or levels of NT-proBNP are useful in the prognostic evaluation ofpatients, regardless of the underlying cause of their disease. Themultimarker risk score is a powerful indicator of severe disease andimminent death, as demonstrated herein in several different heartfailure populations.

Predicting Death

As demonstrated herein, an algorithm that takes into account multiplemarkers including elevated concentrations of soluble ST2 and thesubject's age can be used to accurately predict a subject's risk ofdeath within a specific time period (e.g., within 3 months, within sixmonths, within 1, 2, 5 or 10 years).

General Methodology—Determining a Subject's Multimarker Risk Score

In general, the methods described herein include determining the valuesfor each of the markers in the risk algorithm, including evaluating thelevels (e.g., levels in blood, serum, plasma, urine, or body tissue) ofsoluble ST2 in a subject, e.g., a mammal, e.g., a human; determining thesubject's age, e.g., by querying the subject or the subject's familyfriends, or medical records; and one or more of the following:determining the subject's history of coronary artery disease, e.g., byquerying the subject or the subject's family friends, or medicalrecords, or using routine diagnostic methods; determining the subject'ssystolic blood pressure (SBP); and/or determining one or more of a levelof Troponin; NTpro-BNP; NYHA score; and renal function. These markers,in combination, provide information regarding the subject's likelihoodof mortality, e.g., within a specific time period, e.g., within 3months, 6 months, 1, 2, 5 or 10 years.

Evaluating circulating levels of a marker such as soluble ST2,NTpro-BNP, or troponin in a subject typically includes obtaining abiological sample, e.g., serum, plasma or blood, from the subject.Levels of a marker in the sample can be determined by measuring levelsof polypeptide in the sample, using methods known in the art and/ordescribed herein, e.g., immunoassays such as enzyme-linked immunosorbentassays (ELISA). For example, in some embodiments a monoclonal antibodyis contacted with the sample; binding of the antibody is then detectedand optionally quantified, and levels of the protein are determinedbased on levels of antibody binding. Alternatively, levels of mRNA canbe measured, again using methods known in the art and/or describedherein, e.g., by quantitative PCR or Northern blotting analysis.

In some embodiments, the marker levels or values can then be used in analgorithm to determine a multimarker risk score, e.g., an algorithmdetermined based on statistical analysis of a subject population.Exemplary algorithms include the following:AGE+ln_SBP+CAD+ST2+ln_NTpro-BNP  (1)AGE+ST2+ln_NTpro-BNP  (2)

In these embodiments, the level of soluble ST2 is determined andcompared to a threshold, e.g., 35 or 50 ng/mL, and the presence of alevel at or above the threshold is scored as “1” and the presence of alevel below the threshold is scored as “0”. In some embodiments, inalgorithms (1) and (2) the threshold level of soluble ST2 is 35 ng/mL.AGE+ST2+Troponin+NYHA  (3)AGE+ST2+[Troponin OR NYHA]  (4)

In some embodiments, the level of soluble ST2 is determined and comparedto a threshold, e.g., 35 or 50 ng/mL, and the presence of a level at orabove the threshold is scored as “1” and the presence of a level belowthe threshold is scored as “0”. In some embodiments, in algorithms (3)or (4) the threshold level of ST2 is 50 ng/mL. In some embodiments, thelevel of hemoglobin (Hgb) is also determined, e.g., in an algorithmcomprising:AGE+ST2+[Troponin AND/OR NYHA]+ln_Hgb  (5)

In some embodiments, the NYHA score is determined, and the presence ofan NYHA score at or above a threshold is scored as “1” and the presenceof a level below the threshold is scored as “0”. In some embodiments, inalgorithms (3) or (4) or (5) the threshold score is 3.

In some embodiments, the level of troponin is determined and compared toa threshold, e.g., a level that represents a threshold below whichhealthy individuals fall, and above which individuals are identified ashaving a cardiovascular condition, e.g., 35 or 50 pg/mL, and thepresence of a level at or above the threshold is scored as “1” and thepresence of a level below the threshold is scored as “0”. In someembodiments, in algorithms (3) or (4) or (5) the threshold level oftroponin is 16 pg/mL.

In some embodiments, the multimarker risk score is calculated using acomputing device, e.g., a personal computer.

Once a multimarker risk score has been determined, the multimarker riskscore can be compared to a reference score. In some embodiments, thereference score will represent a threshold level, above which thesubject has an increased risk of death, and/or has a severe disease. Thereference score chosen may depend on the methodology used to measure oneor more of the markers, e.g., the levels of soluble ST2. For example, insome embodiments, where circulating levels of soluble ST2 are determinedusing an immunoassay, e.g., as described herein, and a score above thatreference level indicates that the subject has an increased risk ofdeath.

A reference score can also be a multimarker risk score calculated for ahealthy subject (e.g., a subject not diagnosed with a cardiovasculardisorder (e.g., not diagnosed with heart failure) or not presenting withtwo or more symptoms of a cardiovascular disorder). A reference scorecan also be a multimarker risk score calculated for a subject notdiagnosed with a cardiovascular disorder (e.g., not diagnosed with heartfailure), not presenting with two or more symptoms of a cardiovasculardisorder, and not identified as having an increased risk of developing acardiovascular disorder (e.g., no family history of a cardiovasculardisease).

In some embodiments, more than one multimarker risk score is determinedusing a method described herein, and a change in the score indicateswhether the subject has an increased or decreased risk of death. A scorethat increases means that the subject has an increasing risk of imminentdeath, e.g., an increasingly poor prognosis, and that a treatment is notworking or should be changed or initiated. A score that decreases overtime indicates that the subject has a decreasing risk of imminent death,e.g., an increasingly positive prognosis, and can be indicative of theefficacy of a treatment, for example, and the treatment should becontinued, or, if the score becomes low enough, possibly discontinued.As one example, increasing scores may indicate a need for moreaggressive treatment or hospitalization (e.g., initial admission orhospitalization in a more acute setting, e.g., in an intensive careunit, or the use of telemetry or other methods for monitoring thesubject's cardiac status), while decreasing scores may indicate thepossibility of less aggressive treatment, a short hospitalization, ordischarge. This information allows a treating physician to make moreaccurate treatment decisions; for example, the subject may be admittedto the hospital as an inpatient, e.g., in an acute or critical caredepartment.

Additional testing can be performed, e.g., to determine the subject'sactual condition. More aggressive treatment may be administered eitherbefore or after additional testing. For example, in the case of asuspected myocardial infarction (MI), the subject may be sent for moreextensive imaging studies and/or cardiac catheterization.

In some embodiments, the methods include the use of additionaldiagnostic methods to identify underlying pathology. Any diagnosticmethods known in the art can be used, and one of skill in the art willbe able to select diagnostic methods that are appropriate for thesubject's symptoms. In some embodiments, the methods described hereininclude other diagnostic methods in addition to or as an alternative tothe measurement of other biomarkers, e.g., physical measurements of lungfunction or cardiac function as are known in the art.

In some examples, a subject who has been identified as having anelevated risk of mortality (or one or more of the subject's immediatefamily members) is informed of the symptoms of a cardiovascular disorder(e.g., symptoms of heart failure or MI) and/or are instructed to monitorthe subject for the development or occurrence of one or more symptoms ofcardiovascular disease (e.g., heart failure or MI). In some examples,one or more lineal family members of a subject identified as having anelevated risk of mortality are also tested for the presence of acardiovascular disorder (e.g., heart failure) or methods are performedon such family members to determine their risk of cardiovascular diseaseor their risk of mortality (e.g., using any of the methods describedherein).

ST2

The ST2 gene is a member of the interleukin-1 receptor family, whoseprotein product exists both as a trans-membrane form, as well as asoluble receptor that is detectable in serum (Kieser et al., FEBS Lett.372(2-3): 189-93 (1995); Kumar et al., J. Biol. Chem. 270(46): 27905-13(1995); Yanagisawa et al., FEBS Lett. 302(1): 51-3 (1992); Kuroiwa etal., Hybridoma 19(2): 151-9 (2000)). ST2 was described to be markedlyup-regulated in an experimental model of heart failure (Weinberg et al.,Circulation 106(23): 2961-6 (2002)), and preliminary results suggestthat ST2 concentrations may be elevated in those with chronic severe HF(Weinberg et al., Circulation 107(5): 721-6 (2003)) as well as in thosewith acute myocardial infarction (MI) (Shimpo et al., Circulation109(18): 2186-90 (2004)).

The trans-membrane form of ST2 is thought to play a role in modulatingresponses of T helper type 2 cells (Lohning et al., Proc. Natl. Acad.Sci. U.S.A. 95(12): 6930-5 (1998); Schmitz et al., Immunity 23(5):479-90 (2005)), and may play a role in development of tolerance instates of severe or chronic inflammation (Brint et al., Nat. Immunol.5(4): 373-9 (2004)), while the soluble form of ST2 is up-regulated ingrowth stimulated fibroblasts (Yanagisawa et al., 1992, supra).Experimental data suggest that the ST2 gene is markedly up-regulated instates of myocyte stretch (Weinberg et al., 2002, supra) in a manneranalogous to the induction of the BNP gene (Bruneau et al., Cardiovasc.Res. 28(10): 1519-25 (1994)).

Tominaga, FEBS Lett. 258: 301-304 (1989), isolated murine genes thatwere specifically expressed by growth stimulation in BALB/c-3T3 cells;they termed one of these genes St2. The St2 gene encodes two proteinproducts: ST2, which is a soluble secreted form; and ST2L, atransmembrane receptor form that is very similar to the interleukin-1receptors. The HUGO Nomenclature Committee designated the human homolog,the cloning of which was described in Tominaga et al., Biochim. Biophys.Acta. 1171: 215-218 (1992), as Interleukin 1 Receptor-Like 1 (IL1RL1).The two terms are used interchangeably herein.

The mRNA sequence of the shorter, soluble isoform of human ST2 can befound at GenBank Acc. No. NM_003856.2, and the polypeptide sequence isat GenBank Acc. No. NP_003847.2; the mRNA sequence for the longer formof human ST2 is at GenBank Acc. No. NM_016232.4; the polypeptidesequence is at GenBank Acc. No. NP_057316.3. Additional information isavailable in the public databases at GeneID: 9173, MIM ID #601203, andUniGene No. Hs.66. In general, in the methods described herein, thesoluble form of ST2 polypeptide is measured.

Methods for detecting and measuring ST2 are known in the art, e.g., asdescribed in U.S. Pat. Pub. Nos. 2003/0124624, 2004/0048286 and2005/0130136, the entire contents of which are incorporated herein byreference. Kits for measuring ST2 polypeptide are also commerciallyavailable, e.g., the ST2 ELISA Kit manufactured by Medical & BiologicalLaboratories Co., Ltd. (MBL International Corp., Woburn, Mass.), no.7638. In addition, devices for measuring ST2 and other biomarkers aredescribed in U.S. Pat. Pub. No. 2005/0250156.

Other Biomarkers and Clinical Variables

The methods described herein can also include measuring levels of otherbiomarkers or clinical variables in addition to ST2, including troponinand NT-proBNP. Other markers or clinical variables can also bedetermined, e.g., age, blood pressure, gender, diabetes status, smokingstatus, CRP, IL-6, D-dimers, BUN, liver function enzymes, albumin,measures of renal function, e.g., creatinine, creatinine clearance rate,or glomerular filtration rate, and/or bacterial endotoxin. Methods formeasuring these biomarkers are known in the art, see, e.g., U.S. Pat.Pub. Nos. 2004/0048286 and 2005/0130136 to Lee et al.; Dhalla et al.,Mol. Cell. Biochem. 87: 85-92 (1989); Moe et al., Am. Heart. J. 139:587-95 (2000); Januzzi et al., Eur. Heart J. 27(3): 330-7 (2006); Maiselet al., J. Am. Coll. Cardiol. 44(6): 1328-33 (2004); and Maisel et al.,N. Engl. J. Med. 347(3): 161-7 (2002), the entire contents of which areincorporated herein by reference. Liver function enzymes include alaninetransaminase (ALT); aspartate transaminase (AST); alkaline phosphatase(ALP); and total bilirubin (TBIL).

In these embodiments, a multimarker risk score and levels of one or moreadditional biomarkers are determined, and the information from the scoreand a comparison of the biomarkers with their respective referencelevels provides additional information regarding the subject's risk ofdeath, which may provide more accurate and specific informationregarding the subject's risk. The levels can then be compared to areference level, e.g., a threshold at or above which the subject has anincreased risk of death.

Selecting a Treatment—Aggressive vs. Conservative

Once it has been determined that a subject has a multimarker risk scoreabove a predetermined reference score, the information can be used in avariety of ways. For example, if the subject has an elevated score,e.g., as compared to a reference level, a decision to treat aggressivelycan be made, and the subject can be, e.g., admitted to a hospital fortreatment as an inpatient, e.g., in an acute or critical caredepartment. Portable test kits could allow emergency medical personnelto evaluate a subject in the field, to determine whether they should betransported to the ED. Triage decisions, e.g., in an ED or otherclinical setting, can also be made based on information provided by amethod described herein. Those patients with high scores can beprioritized over those with lower scores. Additional methods forselecting a treatment for a subject based on the determination of asubject's risk or mortality (based on a single multimarker risk score ora first and second multimarker risk score determined for the subject)(e.g., using any of the methods described herein) are known in the artand described herein, e.g., in the Summary section above. Some examplesof any of the methods of selecting a treatment described herein furtherinclude modifying the subject's clinical file (e.g., a computer-readablemedium) to indicate that the subject should be administered the selectedtreatment, admitted to the hospital, discharged from the hospital,continue to be hospitalized, continue to be treated on an outpatientbasis, receive cardiac monitoring (e.g., any of the cardiac monitoringmethods described herein), or receive low frequency monitoring (e.g.,any of the low frequency monitoring methods described herein) (asdetermined using any of the methods described herein). Additionalmethods include administering or performing the selected treatment on asubject.

The methods described herein also provide information regarding whethera subject is improving, e.g., responding to a treatment, e.g., whether ahospitalized subject has improved sufficiently to be discharged andfollowed on an outpatient basis. In general, these methods will includedetermining a multimarker risk score for the subject multiple times. Adecrease in multimarker risk score over time indicates that the subjectis likely to be improving. The most recent multimarker risk score canalso be compared to a reference score, as described herein, to determinewhether the subject has improved sufficiently to be discharged.

The subject may also be considered for inclusion in a clinical trial,e.g., of a treatment that carries a relatively high risk. The subjectcan be treated with a regimen that carries a relatively higher risk thanwould be considered appropriate for someone who had a lower risk ofimminent death, e.g., death within 30 days or within 1 year ofpresentation.

Beyond the clinical setting, information regarding a subject'smultimarker risk score can be used in other ways, e.g., for paymentdecisions by third party payors, or for setting medical or lifeinsurance premiums by insurance providers. For example, a highmultimarker risk score, e.g., a score at or above a predeterminedthreshold score, may be used to decide to increase insurance premiumsfor the subject.

Patient Populations

The methods described herein are useful in the clinical context ofpatients with a cardiovascular disorder (e.g., heart failure). As oneexample, a multimarker risk score can be determined at any time, and ifthe multimarker risk score is elevated, the health care provider can actappropriately. In some embodiments, the methods described herein areused in subjects who have heart failure (HF), e.g., acute decompensated,e.g., heart failure (ADHF) or chronic heart failure (CHF); methods ofdiagnosing HF and ADHF are known in the art.

Computer-Implemented Methods

Any of the methods described herein can be implemented in a system. Forexample, a system can include a processor, memory, and a storage device.The memory can include an operating system (OS), such as Linux, UNIX, orWindows® XP, a TCP/IP stack for communicating with a network (notshown), and a process for calculating one or more multimarker riskscore(s) in accordance with the methods described in this document andalso, optionally, comparing a second determined multiple marker riskscore from a subject at a first time point with a first multiple markerrisk score determined at a first time point or comparing a determinedmultiple marker risk score with a reference value (e.g. a multiplemarker risk score of a healthy subject). In some implementations, thesystem also includes a link to an input/output (I/O) device for displayof a graphical user interface (GUI) to a user. In some implementations,the system is in communication with a user interface which allows aperson to enter clinical information about the patient.

In some implementations, the calculating of the one or more multimarkerrisk score functionality can be implemented within a networkenvironment. For example, a networking environment can provide users(e.g., individuals such as clinicians) access to information collected,produced, and/or stored. Various techniques and methodologies can beimplemented for exchanging information between the users and processor.For example, one or more networks (e.g., the Internet) may be employedfor interchanging information with user devices. Various types ofcomputing devices and display devices may be employed for informationexchange. For example, hand-held computing devices (e.g., a cellulartelephone, tablet computing device, etc.) may exchange informationthrough one or more networks (e.g., the Internet) with the processor.Other types of computing devices such as a laptop computer and othercomputer systems may also be used to exchange information with theprocess for calculating the one or more multiple marker risk score(s). Adisplay device such as a liquid crystal display (LCD) television orother display device may also present information from processor. One ormore types of information protocols (e.g., file transfer protocols,etc.) may be implemented for exchanging information. The user devicesmay also present one or more types of interfaces (e.g., graphical userinterfaces) to exchange information between the user and the processor.For example, a network browser may be executed by a user device toestablish a connection with a website (or webpage) of the processor andprovide a vehicle for exchanging information. The processor can includesoftware and hardware configured to calculate one or more multimarkerrisk score(s) in a subject (e.g., using any of the methods described inthis document).

Operations can further include providing an output as a result of thesubject's risk of mortality or change in risk of mortality. The outputcan be provided, for example, by displaying a representation of theoutput on a display device, or storing data representing the output on acomputer-readable non-transitory storage device. The output can identifyone or more treatments (e.g., any of the treatments described herein)that are selected for the subject, identify a treatment as beingeffective or not effective in the subject, select a subject forparticipation in a clinical study, or identify a subject as having anincreased, decreased, increasing, or decreasing risk of mortality withina specific time period (e.g., according to any of the methods describedherein).

In some examples, a computer device or mobile computer device can beused to implement the techniques described herein. For example, aportion or all of the operations of a comfort modeler may be executed bya computer device (located, for example, within the processor) and/or bythe mobile computer device (that may be operated by an end user).Computing device is intended to represent various forms of digitalcomputers, including, e.g., laptops, desktops, workstations, personaldigital assistants, servers, blade servers, mainframes, and otherappropriate computers. Computing device is intended to represent variousforms of mobile devices, including, e.g., personal digital assistants,cellular telephones, smartphones, and other similar computing devices.The components shown here, their connections and relationships, andtheir functions, are meant to be examples, and are not meant to limitimplementations of the methods described and/or claimed in thisdocument.

A computing device can include a processor, a memory, a storage device,a high-speed interface connecting to memory and high-speed expansionports, and a low speed interface connecting to a low speed bus and astorage device. Each of these components can be interconnected usingvarious busses, and can be mounted on a common motherboard or in othermanners as appropriate. The processor can process instructions forexecution within the computing device, including instructions stored inmemory or on storage device to display graphical data for a GUI on anexternal input/output device, including, e.g., a display coupled to ahigh speed interface. In other implementations, multiple processorsand/or multiple busses can be used, as appropriate, along with multiplememories and types of memory. Also, multiple computing devices can beconnected, with each device providing portions of the necessaryoperations (e.g., as a server bank, a group of blade servers, or amulti-processor system).

A memory that stores data can be within the computing device. In oneimplementation, the memory is a volatile memory unit or units. Inanother implementation, memory is a non-volatile memory unit or units.The memory can also can be another form of non-transitorycomputer-readable medium, including, e.g., a magnetic or optical disk.

The storage device can be capable of providing mass storage for thecomputing device. In one implementation, the storage device can be orcontain a non-transitory computer-readable medium, including, e.g., afloppy disk device, a hard disk device, an optical disk device, or atape device, a flash memory or other similar solid state memory device,or an array of devices, including devices in a storage area network orother configurations. A computer program product can be tangiblyembodied in a data carrier. The computer program product also cancontain instructions that, when executed, perform one or more methods,including, e.g., those described herein. The data carrier can be acomputer- or machine-readable medium, including, e.g., memory, storagedevice, memory on a processor, and the like.

A high-speed controller can be used to manage bandwidth-intensiveoperations for the computing device, while the low speed controller canmanage lower bandwidth-intensive operations. Such allocation offunctions is an example only. In one implementation, a high-speedcontroller can be coupled to a memory, a display (e.g., through agraphics processor or accelerator), and to a high-speed expansion ports,which can accept various expansion cards (not shown). In theimplementation, the low-speed controller can be coupled to a storagedevice and a low-speed expansion port. The low-speed expansion port,which can include various communication ports (e.g., USB, Bluetooth®,Ethernet, wireless Ethernet), can be coupled to one or more input/outputdevices, including, e.g., a keyboard, a pointing device, a scanner, or anetworking device including, e.g., a switch or router, e.g., through anetwork adapter.

As is known in the art, a computing device can be implemented in anumber of different forms. For example, it can be implemented asstandard server, or multiple times in a group of such servers. It alsocan be implemented as part of a personal computer including, e.g.,laptop computer. In some examples, components from the computing devicecan be combined with other components in a mobile device (not shown),including, e.g., device. Each of such devices can contain one or more ofcomputing device(s), and an entire system can be made up of multiplecomputing devices that communicate with each other.

A computing device can include a processor, a memory, an input/outputdevice including, e.g., a display, a communication interface, and atransceiver, among other components. The device also can be providedwith a storage device, including, e.g., a microdrive or other device, toprovide additional storage. Each of these components can beinterconnected using various busses, and several of the components canbe mounted on a common motherboard or in other manners as appropriate.

The processor can execute instructions within the computing device,including instructions stored in the memory. The processor can beimplemented as a chipset of chips that include separate and multipleanalog and digital processors. The processor can provide, for example,for coordination of the other components of the device, including, e.g.,control of user interfaces, applications run by the device, and wirelesscommunication by the device.

The processor can communicate with a user through a control interfaceand a display interface coupled to the display. The display can be, forexample, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or anOLED (Organic Light Emitting Diode) display, or other appropriatedisplay technology. The display interface can include appropriatecircuitry for driving display to present graphical and other data to auser. The control interface can also receive commands from a user andconvert them for submission to processor. In addition, an externalinterface can communicate with processor, so as to enable near areacommunication of device with other devices. The external interface canprovide, for example, for wired communication in some implementations,or for wireless communication in other implementations, and multipleinterfaces also can be used.

The memory can store data within the computing device. The memory can beimplemented as one or more of a computer-readable medium or media, avolatile memory unit or units, or a non-volatile memory unit or units.An expansion memory can also be provided and connected to the devicethrough an expansion interface, which can include, for example, a SIMM(Single In Line Memory Module) card interface. Such expansion memory canprovide extra storage space for the device, or also can storeapplications or other data for the device. Specifically, the expansionmemory can include instructions to carry out or supplement the processesdescribed above, and can also include secure data. Thus, for example,the expansion memory can be provided as a security module for thedevice, and can be programmed with instructions that permit secure useof the device. In addition, secure applications can be provided throughthe SIMM cards, along with additional data, including, e.g., placingidentifying data on the SIMM card in a non-hackable manner.

The memory can include, for example, flash memory and/or NVRAM memory,as discussed below. In one implementation, a computer program product istangibly embodied in a data carrier. The computer program productcontains instructions that, when executed, perform one or more methods,including, e.g., any of the methods described herein. The data carrieris a computer- or machine-readable medium, including, e.g., memory,expansion memory, and/or memory on a processor that can be received, forexample, over a transceiver or an external interface.

The device can communicate wirelessly through a communication interface,which can have multimarker risk score calculating circuitry wherenecessary, or where desired. The communication interface can provide forcommunications under various modes or protocols, including, e.g., GSMvoice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA,CDMA2000, or GPRS, among others. Such communication can occur, forexample, through a radio-frequency transceiver. In addition, short-rangecommunication can occur, including, e.g., using a Bluetooth®, WiFi, orother such transceiver (not shown). In addition, a GPS (GlobalPositioning System) receiver module can provide additional navigation-and location-related wireless data to the device, which can be used asappropriate by applications running on the device.

The device can also communicate audibly using an audio codec, which canreceive spoken data from a user and convert it to usable digital data.The audio code can likewise generate audible sound for a user,including, e.g., through a speaker, e.g., in a handset of device. Suchsound can include sound from voice telephone calls, can include recordedsound (e.g., voice messages, music files, and the like) and also caninclude sound generated by applications operating on the device.

As is known in the art, the computing device can be implemented in anumber of different forms. For example, it can be implemented ascellular telephone. It also can be implemented as part of smartphone,personal digital assistant, or other similar mobile device.

Various implementations of any of the systems and methods describedherein can be realized in digital electronic circuitry, integratedcircuitry, specially designed ASICs (application specific integratedcircuits), computer hardware, firmware, software, and/or combinationsthereof. These various implementations can include implementation in oneor more computer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichcan be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device. Thesecomputer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to a computer program product, apparatusand/or device (e.g., magnetic discs, optical disks, memory, ProgrammableLogic Devices (PLDs)) used to provide machine instructions and/or datato a programmable processor, including a machine-readable medium thatreceives machine instructions.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying data to the user and a keyboard and a pointing device(e.g., a mouse or a trackball) by which the user can provide input tothe computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be a form of sensory feedback (e.g., visual feedback, auditoryfeedback, or tactile feedback); and input from the user can be receivedin a form, including acoustic, speech, or tactile input. Any of thesystems and methods described herein can be implemented in a computingsystem that includes a back end component (e.g., as a data server), orthat includes a middleware component (e.g., an application server), orthat includes a front end component (e.g., a client computer having auser interface or a Web browser through which a user can interact withan implementation of any of the systems and methods described herein),or a combination of such back end, middleware, or front end components.The components of the system can be interconnected by a form or mediumof digital data communication (e.g., a communication network). Examplesof communication networks include: a local area network (LAN), a widearea network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

EXAMPLES

The invention is further described in the following examples, which donot limit the scope of the invention described in the claims.

Example 1. Predictive Model Based on Barcelona Study

The objective of this example was to construct a model of heart failurefrom data in the Barcelona Cohort, to predict 1 Year Mortality and Study(5 Year) Mortality.

Summary of study. The Barcelona Study was a prospective, blinded studyof 891 ambulatory patients referred to the Heart Failure unit integratedinto a tertiary-care hospital. Most patients were referred fromcardiology (70.5%) and internal medicine (15.1%); 5% come from theemergency room or short-stay unit. Admissions from primary care clinicswere few.

Enrollment criteria. Patients were enrolled who had either been referredto the Heart Failure unit for Heart Failure, independent of etiology, orwho had severely depressed ventricular function following acutemyocardial infarction (AMI).

Patient assessment. All subjects underwent a clinical assessment thatincluded relevant history, detailed physical examination,echocardiogram, and blood work-up. A diagnosis of heart failure wasconfirmed by physician clinical assessment.

Biochemical sampling information. Venous blood samples were obtained atstudy enrollment, processed, and stored at −80° C. until time of thePresage ST2 Assay measurement.

This study conformed to the principles of the Declaration of Helsinkiand was approved of by the local ethical committees. All participantsprovided written, informed consent.

Clinical Program Study Cohort. All of the 891 participants of theBarcelona study were included in the Presage ST2 Assay Clinical ProgramStudy Cohort. Across these patients, 78 patients (8.8%) reached the endpoint of all-cause mortality within one year.

The models were created based on the following quantitative variables:Age; ST2; left ventricular ejection fraction (LVEF); body mass index(BMI); NT-proBNP; Troponin (cTnT1); Creatinine; Estimated GlomerularFiltration Rate (eGFR); systolic blood pressure (SBP); diastolic bloodpressure (DBP); and Hemoglobin (Hgb), and the following discretevariables: New York Heart Association (NYHA) score; Ethnicity; Sex;history of Coronary Artery Disease (CAD); Diabetes; and hypertension(HTN).

The following statistical measures were made: Median's[IQR]; Differencesbetween Events and Censored; Standardized HR—raw and In transformed;AUC; Normality Test (Shapiro Wilks Test). Discrete variables wereevaluated with counts and HR. The results are shown in FIGS. 1 and 2.Linearity Checks and Cut-point Evaluations were also performed, seeFIGS. 3-24, with a summary in FIG. 25. Based on this analysis, a set ofvariables was defined that included the variables shown in FIG. 26.

The model was constructed by analysis of all combinations of thevariables shown in FIG. 26, and all models of size 1-5 were selected.Fit parameters (e.g. AIC and BIC) were estimated, as was discrimination(AUC). An estimate of over-fit was made using bootstrap analysis. A 3 or5 parameter model was selected to reduce the likelihood of overfitunless there is a systematic bias in the data set.

Several heuristic approaches were used to identify the best models,including backward, forward and stepwise selection, and selection wasmade based on AIC (Akaike's Information Criteria) or BIC (BayesianInformation Criteria).

The results are shown in FIGS. 27-34. For the 1-year outcome models, thebest small models consist of Age, ST2, Troponin and NYHA>=3 with abootstrapped performance of ˜0.79; 3 parameter models contain ST2, Age+1other marker with a bootstrapped performance of ˜0.78. Marker selectionbased on AIC resulted in models that were over-fit. Marker selectionbased on BIC consisted of Troponin, Age, ST2>=50, NYHA>=3, Troponin>=16,and Hgb, with a bootstrapped performance of ˜0.80. For the studyoutcomes, the best small models consist of Age, ST2>=50, Troponin andNYHA>=3+1 marker with a bootstrapped performance of 0.81-0.82; 3parameter models contain Age (10), ST2 (8), Trroponin (7), or NHYA (5)with a bootstrapped performance of 0.79-0.80. Marker selection based onAIC again resulted in models that were over-fit, and marker selectionbased on BIC consisted of Troponin, ST2, Age, and NYHA>=3 with abootstrapped performance of 0.79-0.80.

Example 2. Predictive Model Based on PRIDE Study

The objective of the study described in this example was to develop analgorithm capable of predicting 1 year mortality in subjects that areADHF positive. There were 148 Controls and 61 Cases; the data set issufficient to support a model of 3-6 parameters.

Summary of Parent Study. The PRoBNP Investigation of Dyspnea in theEmergency Department study (PRIDE) was a prospective, blinded study of599 dyspneic subjects presenting to the Emergency Department (ED) of theMassachusetts General Hospital, in Boston, Mass. PRIDE was performed forthe purpose of validating use of NT-proBNP testing (using the predicatedevice Elecsys ProBNP, Roche Diagnostics, Indianapolis, Ind.). Thecomplete selection criteria and design of the PRIDE study have beendescribed previously in peer-reviewed publications (Januzzi et al. 2005,Januzzi et al. 2006).

Enrollment criteria. Original PRIDE enrollment criteria included allpatients at least 21 years of age presenting to the ED with complaintsof dyspnea.

Original exclusion criteria were dyspnea following blunt or penetratingtrauma to the chest, renal failure (serum creatinine>2.5 mg/dl), STelevation myocardial infarction, or electrocardiographic changesdiagnostic of acute coronary ischemia, such as ST segment depression ortransient ST segment elevation in the presence of symptoms suggestive ofcoronary artery disease.

Other exclusions included treatment with an acute dose (non-maintenancetherapy) of a loop diuretic more than two hours prior to enrollment, andpatient unwillingness or inability to provide written informed consent(or site otherwise unable to obtain informed consent from available nextof kin).

Patient Assessment. Diagnosis was recorded by the ED physician as wellas by the attending physician following admission, both blinded to thebiomarker concentrations. In the event of a disagreement between the twoprimary physicians, two of the three cardiologists involved in the studyadjudicated patient diagnosis as either congestive heart failure ordyspnea due to non-cardiac cause.

Using these criteria, 599 patients were enrolled at the single site. Ofthe 599 patients, 209 (34.8%) had an adjudicated diagnosis of congestiveheart failure. All patients were monitored for one year for all causemortality.

Biochemical sampling information. Blood samples (EDTA plasma) werecollected at presentation and stored at −80° C. for analysis until thetime of the Presage ST2 Assay measurement.

All participants provided written, informed consent, and the PRIDEprotocol was approved by the participating Institutional Review Board.

Presage ST2 Assay Clinical Program Cohort. The Clinical Program includesonly the 209 patients diagnosed with acute heart failure, using the allcause mortality endpoint. Across these patients, 61 patients (29.1%)reached the end point of all cause mortality within one year.

The potential parameters included measurements of ST2, NT-proBNP,Troponin, Age, Renal Function (Creatinine or eGFR), Hemoglobin, andBlood Pressure (e.g., systolic or diastolic BP). Additional parametersincluded Gender, Ethnicity, BMI, Hypertension, Diabetes, CAD, andC-reactive protein (CRP).

The modeling approach was based on logistic regression, which is alinear model with an output of the log odds of having an event, and isdirectly related to probability of an event (i.e. risk). The followingassumptions were made: a linear relationship between risk (y) and X; themarkers included in the model are mutually exclusive (independent or notco-linear; a correlation coefficient around 0.7 or higher is usuallyconsidered as evidence of colinearity); the markers should becollectively exhaustive (though this assumption is typically relaxed asit is difficult to know what markers might be missing).

Covariance among the markers was evaluated, as was linearity of theresponse to risk. Transforms or non-linear terms were considered, andthe markers were combined and selected under a bootstrap analysis toestimate true performance. The model performance was also evaluatedunder a bootstrap analysis.

The results of the colinearity analysis are shown in FIG. 35; nosignificant colinearity was found, except among the markers of renalfunction. Univariate performance of the various markers is shown in FIG.36. Results of the linearity check are shown in FIGS. 37-49. A summaryof the results and variables is shown in FIG. 50.

The model was then created. Missing values were imputed to strengthenthe data set, and markers were selected within a bootstrap loop, usingforward selection, backward selection, stepwise forward, and stepwisebackward selection. Performance and marker selection were tracked.

The final model size as determined by AIC and BIC was too large, asshown on FIGS. 51 and 52, so combinatorics were used to improve themodel. All of the models (a total of 60,459) of size 1-6 were evaluatedand the best was selected based on AIC/BIC. The ten best AIC Models allcontained Age, LN_SBP, CAD, and ST2>=35; 9 contain LN_NTBNP. Nine of themodels had size=6 (1 of size=5). The ten best BIC Models all containedAge; 7 contain LN_NTBNP, and 8 contain ST2>=35. The BIC models were muchsmaller (k=2(3), k=3(6), k=4(1).

Two models were selected as the best. The first[Age+LN_SBP+CAD+ST2>=35+LN_NTBNP] had a fitted AUC=0.791, and the second[Age+ST2>=35+LN_NTBNP] had a fitted AUC=0.755 (pr(ROC1=ROC2)=0.0714).The second model was more discriminating than NTPro Alone (AUC=0.68;p=0.181), ST2 alone (AUC=0.692; p=0.233), and a model of ST2 and BNP(AUC=0.721; p=0.2735). Comparisons of the two models are shown in FIGS.53-54.

As shown in FIG. 55, when compared with the “out of bag” estimates, thefive parameter Model had a Median AUC=0.758 (IQR: 0.726-0.788). Thethree Parameter Model had a Median AUC=0.738 (IQR:0.707-0.769). The 5parameter model had a higher AUC on 77.5% of the replicates. Modelcalibration, shown in FIG. 56, was close to expected (red), as isusually the case when a training population is used.

Assuming a median split in the 5 parameter model, the model had aSensitivity=0.79, Specificity=0.62, PPV=0.46, NPV=0.88, and OddsRatio=6.0. Exemplary Model Parameters are shown in FIG. 57.

Other Embodiments

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

What is claimed is:
 1. A method for treating a subject having heartfailure, the method comprising: (a) determining a first multimarkermortality risk score for a subject having heart failure at a first timepoint using an algorithm selected from the group consisting of:age of the subject(AGE)+the level of soluble ST2 in the subject(ST2)+anatural logarithm of a systolic blood pressure(ln_SBP)+a history ofcardiovascular disease(CAD)+a natural logarithm of a level of N-terminalpro-brain natriuretic peptide(ln_NTpro-BNP);  (1)AGE+ST2+ln_NTpro-BNP;  (2)AGE+ST2+a level of troponin in the subject(Troponin)+a New York HeartAssociation score(NYHA);  (3)AGE+ST2+[Troponin OR NYHA];  (4)AGE+ST2+[Troponin AND/OR NYHA]+a natural logarithm of a level ofhemoglobin(ln_Hgb);  (5)AGE+ST2+[Troponin AND/OR NYHA]+ln_Hgb;  (6)AGE+ST2+[Troponin AND/OR NYHA]+ln_Hgb+ln_SBP; and  (7)AGE+ST2+[Troponin AND/OR NYHA]+ln_Hgb+ln_SBP+ln_NTpro-BNP;  (8) (b)determining a second multimarker mortality risk score for the subject ata second time point using the algorithm in step (a); (c) comparing thesecond multimarker mortality risk score to the first multimarkermortality risk score; and (d) administering inpatient treatment to asubject having an elevated second multimarker mortality risk score ascompared to the first multimarker mortality risk score.
 2. The method ofclaim 1, wherein the level of soluble ST2 is compared to a threshold andthe presence of a level at or above the threshold is scored as “1” andthe presence of a level below the threshold is scored as “0”.
 3. Themethod of claim 2, wherein the threshold is 35 or 50 ng/mL.
 4. Themethod of claim 3, wherein algorithm (1) or (2) is used and thethreshold level of ST2 is 35 ng/mL.
 5. The method of claim 3, whereinalgorithm (3) or (4) is used and the threshold level of ST2 is 50 ng/mL.6. The method of claim 1, wherein the subject has been diagnosed withheart failure.
 7. The method of claim 1, wherein the subject has a BMIof 25-29, a BMI of ≥30, or renal insufficiency.
 8. The method of claim1, wherein the first multimarker mortality risk score and the secondmultimarker risk score are determined for the subject using algorithm(1).
 9. The method of claim 1, wherein the first multimarker mortalityrisk score and the second multimarker mortality risk score aredetermined for the subject using algorithm (2).
 10. The method of claim1, wherein the first multimarker mortality risk score and the secondmultimarker mortality risk score are determined for the subject usingalgorithm (3).
 11. The method of claim 1, wherein the first multimarkermortality risk score and the second multimarker risk score aredetermined for the subject using algorithm (4).
 12. The method of claim1, wherein the first multimarker mortality risk score and the secondmultimarker risk score are determined for the subject using algorithm(5).
 13. The method of claim 1, wherein the first multimarker mortalityrisk score and the second multimarker mortality risk score aredetermined for the subject using algorithm (6).
 14. The method of claim1, wherein the first multimarker mortality risk score and secondmultimarker mortality risk score are determined for the subject usingalgorithm (7).
 15. The method of claim 1, wherein the first multimarkermortality risk score and the second multimarker mortality risk score aredetermined for the subject using algorithm (8).